Overview

Dataset statistics

Number of variables12
Number of observations1599
Missing cells0
Missing cells (%)0.0%
Duplicate rows212
Duplicate rows (%)13.3%
Total size in memory150.0 KiB
Average record size in memory96.1 B

Variable types

Numeric12

Alerts

Dataset has 212 (13.3%) duplicate rowsDuplicates
fixed acidity is highly correlated with citric acid and 2 other fieldsHigh correlation
volatile acidity is highly correlated with citric acidHigh correlation
citric acid is highly correlated with fixed acidity and 2 other fieldsHigh correlation
free sulfur dioxide is highly correlated with total sulfur dioxideHigh correlation
total sulfur dioxide is highly correlated with free sulfur dioxideHigh correlation
density is highly correlated with fixed acidityHigh correlation
pH is highly correlated with fixed acidity and 1 other fieldsHigh correlation
fixed acidity is highly correlated with citric acid and 2 other fieldsHigh correlation
volatile acidity is highly correlated with citric acidHigh correlation
citric acid is highly correlated with fixed acidity and 2 other fieldsHigh correlation
free sulfur dioxide is highly correlated with total sulfur dioxideHigh correlation
total sulfur dioxide is highly correlated with free sulfur dioxideHigh correlation
density is highly correlated with fixed acidityHigh correlation
pH is highly correlated with fixed acidity and 1 other fieldsHigh correlation
fixed acidity is highly correlated with pHHigh correlation
free sulfur dioxide is highly correlated with total sulfur dioxideHigh correlation
total sulfur dioxide is highly correlated with free sulfur dioxideHigh correlation
pH is highly correlated with fixed acidityHigh correlation
fixed acidity is highly correlated with citric acid and 3 other fieldsHigh correlation
citric acid is highly correlated with fixed acidity and 3 other fieldsHigh correlation
residual sugar is highly correlated with densityHigh correlation
chlorides is highly correlated with citric acid and 1 other fieldsHigh correlation
free sulfur dioxide is highly correlated with total sulfur dioxideHigh correlation
total sulfur dioxide is highly correlated with free sulfur dioxideHigh correlation
density is highly correlated with fixed acidity and 3 other fieldsHigh correlation
pH is highly correlated with fixed acidity and 2 other fieldsHigh correlation
sulphates is highly correlated with citric acid and 1 other fieldsHigh correlation
alcohol is highly correlated with fixed acidity and 1 other fieldsHigh correlation

Reproduction

Analysis started2023-04-09 20:20:27.439721
Analysis finished2023-04-09 20:21:12.677995
Duration45.24 seconds
Software versionpandas-profiling v3.2.0
Download configurationconfig.json

Variables

fixed acidity
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct96
Distinct (%)6.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.599372207 × 10-16
Minimum-2.137044857
Maximum4.355149169
Zeros0
Zeros (%)0.0%
Negative969
Negative (%)60.6%
Memory size12.6 KiB
2023-04-09T20:21:12.859465image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum-2.137044857
5-th percentile-1.27524919
Q1-0.7007187452
median-0.2410943893
Q30.505795189
95-th percentile1.999574346
Maximum4.355149169
Range6.492194027
Interquartile range (IQR)1.206513934

Descriptive statistics

Standard deviation1.000312842
Coefficient of variation (CV)2.779131428 × 1015
Kurtosis1.132143398
Mean3.599372207 × 10-16
Median Absolute Deviation (MAD)0.5745304448
Skewness0.9827514413
Sum5.684341886 × 10-13
Variance1.000625782
MonotonicityNot monotonic
2023-04-09T20:21:13.148237image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-0.643265700767
 
4.2%
-0.700718745257
 
3.6%
-0.298547433853
 
3.3%
-0.470906567252
 
3.3%
-0.758171789750
 
3.1%
-0.356000478349
 
3.1%
-0.873077878646
 
2.9%
-0.413453522846
 
2.9%
-0.0687352558545
 
2.8%
-0.528359611744
 
2.8%
Other values (86)1090
68.2%
ValueCountFrequency (%)
-2.1370448571
 
0.1%
-2.0795918131
 
0.1%
-1.9646857241
 
0.1%
-1.9072326796
0.4%
-1.8497796354
 
0.3%
-1.792326596
0.4%
-1.7348735464
 
0.3%
-1.6774205015
 
0.3%
-1.6199674571
 
0.1%
-1.56251441214
0.9%
ValueCountFrequency (%)
4.3551491691
0.1%
4.1827900362
0.1%
4.1253369922
0.1%
3.8380717692
0.1%
3.4359004581
0.1%
3.2635413241
0.1%
3.1486352351
0.1%
3.0911821912
0.1%
2.9762761021
0.1%
2.9188230571
0.1%

volatile acidity
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION

Distinct144
Distinct (%)9.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-2.843948411 × 10-16
Minimum-2.818740701
Maximum5.692614089
Zeros0
Zeros (%)0.0%
Negative819
Negative (%)51.2%
Memory size12.6 KiB
2023-04-09T20:21:13.476774image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum-2.818740701
5-th percentile-1.418138014
Q1-0.7178366708
median-0.01753532733
Q30.628896682
95-th percentile1.706283364
Maximum5.692614089
Range8.51135479
Interquartile range (IQR)1.346733353

Descriptive statistics

Standard deviation1.000312842
Coefficient of variation (CV)-3.517338213 × 1015
Kurtosis1.269831833
Mean-2.843948411 × 10-16
Median Absolute Deviation (MAD)0.6464320094
Skewness0.4745800227
Sum-3.907985047 × 10-13
Variance1.000625782
MonotonicityNot monotonic
2023-04-09T20:21:13.882061image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.413419345647
 
2.9%
-0.125273995646
 
2.9%
-0.502359334443
 
2.7%
0.359550011539
 
2.4%
0.305680677438
 
2.4%
-0.879444673238
 
2.4%
-0.663967336737
 
2.3%
-0.771706004935
 
2.2%
-0.179143329735
 
2.2%
-0.717836670835
 
2.2%
Other values (134)1206
75.4%
ValueCountFrequency (%)
-2.81874070114
0.9%
-2.1723086923
 
0.2%
-1.9568313552
 
0.1%
-1.84909268710
0.6%
-1.7952233532
 
0.1%
-1.7413540193
 
0.2%
-1.6874846856
0.4%
-1.6336153516
0.4%
-1.5797460175
 
0.3%
-1.52587668313
0.8%
ValueCountFrequency (%)
5.6926140891
 
0.1%
4.3458807362
0.1%
3.8610567291
 
0.1%
3.5647753911
 
0.1%
3.5378407241
 
0.1%
3.2684940541
 
0.1%
3.1876900531
 
0.1%
3.0530167171
 
0.1%
2.9452780491
 
0.1%
2.7836700473
0.2%

citric acid
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct80
Distinct (%)5.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-3.110568574 × 10-17
Minimum-1.391472278
Maximum3.743573932
Zeros0
Zeros (%)0.0%
Negative852
Negative (%)53.3%
Memory size12.6 KiB
2023-04-09T20:21:14.395676image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum-1.391472278
5-th percentile-1.391472278
Q1-0.9293181188
median-0.05636026327
Q30.7652471302
95-th percentile1.689555448
Maximum3.743573932
Range5.135046209
Interquartile range (IQR)1.694565249

Descriptive statistics

Standard deviation1.000312842
Coefficient of variation (CV)-3.215852081 × 1016
Kurtosis-0.7889975154
Mean-3.110568574 × 10-17
Median Absolute Deviation (MAD)0.8729578556
Skewness0.3183372953
Sum-1.207922651 × 10-13
Variance1.000625782
MonotonicityNot monotonic
2023-04-09T20:21:14.913685image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-1.391472278132
 
8.3%
1.12470036568
 
4.3%
-0.159061187551
 
3.2%
-1.28877135350
 
3.1%
-0.0563602632738
 
2.4%
-0.877967656735
 
2.2%
-0.980668580933
 
2.1%
-0.313112573733
 
2.1%
-1.34012181633
 
2.1%
0.251742509332
 
2.0%
Other values (70)1094
68.4%
ValueCountFrequency (%)
-1.391472278132
8.3%
-1.34012181633
 
2.1%
-1.28877135350
 
3.1%
-1.23742089130
 
1.9%
-1.18607042929
 
1.8%
-1.13471996720
 
1.3%
-1.08336950524
 
1.5%
-1.03201904322
 
1.4%
-0.980668580933
 
2.1%
-0.929318118830
 
1.9%
ValueCountFrequency (%)
3.7435739321
 
0.1%
2.6652142281
 
0.1%
2.6138637661
 
0.1%
2.5111628413
0.2%
2.4598123791
 
0.1%
2.4084619174
0.3%
2.3571114553
0.2%
2.3057609931
 
0.1%
2.2544105311
 
0.1%
2.2030600692
0.1%

residual sugar
Real number (ℝ)

HIGH CORRELATION

Distinct92
Distinct (%)5.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-6.387774751 × 10-17
Minimum-1.766791648
Maximum9.099651083
Zeros0
Zeros (%)0.0%
Negative1164
Negative (%)72.8%
Memory size12.6 KiB
2023-04-09T20:21:15.448075image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum-1.766791648
5-th percentile-0.7152004157
Q1-0.4347760872
median-0.2244578408
Q30.05596648771
95-th percentile1.808618541
Maximum9.099651083
Range10.86644273
Interquartile range (IQR)0.4907425749

Descriptive statistics

Standard deviation1.000312842
Coefficient of variation (CV)-1.565980144 × 1016
Kurtosis27.44721975
Mean-6.387774751 × 10-17
Median Absolute Deviation (MAD)0.2103182464
Skewness4.377839208
Sum-6.838973832 × 10-14
Variance1.000625782
MonotonicityNot monotonic
2023-04-09T20:21:15.900744image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-0.3646700051156
 
9.8%
-0.2244578408131
 
8.2%
-0.5048821694129
 
8.1%
-0.294563923128
 
8.0%
-0.4347760872117
 
7.3%
-0.1543517587109
 
6.8%
-0.0842456765684
 
5.3%
0.0559664877179
 
4.9%
-0.574988251576
 
4.8%
-0.0141395944374
 
4.6%
Other values (82)516
32.3%
ValueCountFrequency (%)
-1.76679164812
 
0.8%
-1.1358369092
 
0.1%
-0.92551866218
 
0.5%
-0.855412585
 
0.3%
-0.785306497935
2.2%
-0.715200415730
 
1.9%
-0.645094333658
3.6%
-0.61004129262
 
0.1%
-0.574988251576
4.8%
-0.53993521042
 
0.1%
ValueCountFrequency (%)
9.0996510831
0.1%
9.0295450012
0.1%
7.9779537691
0.1%
7.9078476872
0.1%
7.6274233581
0.1%
7.2768929471
0.1%
5.9448773872
0.1%
5.734559141
0.1%
4.5427557441
0.1%
4.4726496621
0.1%

chlorides
Real number (ℝ)

HIGH CORRELATION

Distinct153
Distinct (%)9.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.554935513 × 10-16
Minimum-1.603944891
Maximum11.12703455
Zeros0
Zeros (%)0.0%
Negative1125
Negative (%)70.4%
Memory size12.6 KiB
2023-04-09T20:21:16.378386image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum-1.603944891
5-th percentile-0.7112885694
Q1-0.3712290183
median-0.1799455208
Q30.05384542064
95-th percentile0.8211047829
Maximum11.12703455
Range12.73097945
Interquartile range (IQR)0.4250744389

Descriptive statistics

Standard deviation1.000312842
Coefficient of variation (CV)2.813870571 × 1015
Kurtosis41.71578725
Mean3.554935513 × 10-16
Median Absolute Deviation (MAD)0.2125372194
Skewness5.680346572
Sum5.69322367 × 10-13
Variance1.000625782
MonotonicityNot monotonic
2023-04-09T20:21:16.889929image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-0.158691798866
 
4.1%
-0.286214130555
 
3.4%
-0.243706686651
 
3.2%
-0.201199242751
 
3.2%
-0.0736769110349
 
3.1%
-0.349975296347
 
2.9%
-0.222452964647
 
2.9%
-0.116184354946
 
2.9%
-0.264960408545
 
2.8%
-0.179945520843
 
2.7%
Other values (143)1099
68.7%
ValueCountFrequency (%)
-1.6039448912
 
0.1%
-1.1363630081
 
0.1%
-1.0513481212
 
0.1%
-1.0300943994
0.3%
-0.98758695474
0.3%
-0.96633323273
0.2%
-0.94507951081
 
0.1%
-0.92382578885
0.3%
-0.90257206694
0.3%
-0.88131834494
0.3%
ValueCountFrequency (%)
11.127034551
 
0.1%
11.105780831
 
0.1%
8.0664985941
 
0.1%
8.0027374281
 
0.1%
7.1100811061
 
0.1%
6.9613050533
0.2%
6.9400513312
0.1%
6.9187976091
 
0.1%
6.7062603891
 
0.1%
6.6637529461
 
0.1%

free sulfur dioxide
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct60
Distinct (%)3.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-6.498866486 × 10-17
Minimum-1.422500195
Maximum5.367284318
Zeros0
Zeros (%)0.0%
Negative924
Negative (%)57.8%
Memory size12.6 KiB
2023-04-09T20:21:17.186857image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum-1.422500195
5-th percentile-1.135607892
Q1-0.8487155883
median-0.1793002138
Q30.4901151607
95-th percentile1.82894591
Maximum5.367284318
Range6.789784513
Interquartile range (IQR)1.338830749

Descriptive statistics

Standard deviation1.000312842
Coefficient of variation (CV)-1.539211252 × 1016
Kurtosis2.023562046
Mean-6.498866486 × 10-17
Median Absolute Deviation (MAD)0.6694153745
Skewness1.250567293
Sum-1.199040867 × 10-13
Variance1.000625782
MonotonicityNot monotonic
2023-04-09T20:21:17.474193image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-0.9443463561138
 
8.6%
-1.039977124104
 
6.5%
-0.561823284979
 
4.9%
-0.0836694459978
 
4.9%
-0.370561749375
 
4.7%
-0.848715588371
 
4.4%
-0.657454052762
 
3.9%
0.011961321861
 
3.8%
0.107592089660
 
3.8%
-0.466192517159
 
3.7%
Other values (50)812
50.8%
ValueCountFrequency (%)
-1.4225001953
 
0.2%
-1.3268694271
 
0.1%
-1.23123865949
 
3.1%
-1.13560789241
 
2.6%
-1.039977124104
6.5%
-0.99216173991
 
0.1%
-0.9443463561138
8.6%
-0.848715588371
4.4%
-0.753084820556
3.5%
-0.657454052762
3.9%
ValueCountFrequency (%)
5.3672843181
 
0.1%
4.9847612472
0.1%
4.7934997111
 
0.1%
3.9328228011
 
0.1%
3.7415612652
0.1%
3.6459304981
 
0.1%
3.550299731
 
0.1%
3.4546689623
0.2%
3.3590381944
0.3%
3.2634074272
0.1%

total sulfur dioxide
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct144
Distinct (%)9.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.109871027 × 10-17
Minimum-1.23058377
Maximum7.37515394
Zeros0
Zeros (%)0.0%
Negative976
Negative (%)61.0%
Memory size12.6 KiB
2023-04-09T20:21:17.794142image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum-1.23058377
5-th percentile-1.078538934
Q1-0.744040295
median-0.2574968202
Q30.4723183919
95-th percentile1.995807647
Maximum7.37515394
Range8.60573771
Interquartile range (IQR)1.216358687

Descriptive statistics

Standard deviation1.000312842
Coefficient of variation (CV)1.406935285 × 1016
Kurtosis3.809824488
Mean7.109871027 × 10-17
Median Absolute Deviation (MAD)0.5473614091
Skewness1.515531258
Sum7.105427358 × 10-14
Variance1.000625782
MonotonicityNot monotonic
2023-04-09T20:21:18.100480image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-0.561586491943
 
2.7%
-0.683222360636
 
2.3%
-0.956903065235
 
2.2%
-0.865676163735
 
2.2%
-0.713631327834
 
2.1%
-0.987312032333
 
2.1%
-0.804858229333
 
2.1%
-0.470359590432
 
2.0%
-0.257496820231
 
1.9%
-0.591995459130
 
1.9%
Other values (134)1257
78.6%
ValueCountFrequency (%)
-1.230583773
 
0.2%
-1.2001748034
 
0.3%
-1.16976583514
 
0.9%
-1.13935686814
 
0.9%
-1.10894790127
1.7%
-1.07853893426
1.6%
-1.04812996729
1.8%
-1.01772128
1.8%
-0.987312032333
2.1%
-0.956903065235
2.2%
ValueCountFrequency (%)
7.375153941
0.1%
7.0406553011
0.1%
3.6044420111
0.1%
3.4523971751
0.1%
3.3003523391
0.1%
3.2395344051
0.1%
3.2091254371
0.1%
3.178716472
0.1%
3.1178985361
0.1%
3.0874895692
0.1%

density
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct436
Distinct (%)27.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-3.470505795 × 10-14
Minimum-3.538731398
Maximum3.680055125
Zeros0
Zeros (%)0.0%
Negative798
Negative (%)49.9%
Memory size12.6 KiB
2023-04-09T20:21:18.393335image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum-3.538731398
5-th percentile-1.668843083
Q1-0.607755666
median0.001760083001
Q30.5768249418
95-th percentile1.724304591
Maximum3.680055125
Range7.218786523
Interquartile range (IQR)1.184580608

Descriptive statistics

Standard deviation1.000312842
Coefficient of variation (CV)-2.882325809 × 1013
Kurtosis0.9340790655
Mean-3.470505795 × 10-14
Median Absolute Deviation (MAD)0.5989154751
Skewness0.07128766295
Sum-5.551933913 × 10-11
Variance1.000625782
MonotonicityNot monotonic
2023-04-09T20:21:18.687416image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.240266245736
 
2.3%
0.0282607677435
 
2.2%
0.452271723635
 
2.2%
0.664277201529
 
1.8%
-0.289747449128
 
1.8%
0.558274462526
 
1.6%
-0.183744710225
 
1.6%
1.40629637424
 
1.5%
0.134263506724
 
1.5%
0.770279940423
 
1.4%
Other values (426)1314
82.2%
ValueCountFrequency (%)
-3.5387313982
0.1%
-3.4698296181
0.1%
-3.2366235922
0.1%
-3.1518214011
0.1%
-3.1306208531
0.1%
-2.9398159231
0.1%
-2.7808118151
0.1%
-2.7596112671
0.1%
-2.7437108561
0.1%
-2.7278104452
0.1%
ValueCountFrequency (%)
3.6800551252
0.1%
3.4203484141
 
0.1%
3.393847733
0.2%
3.2560441691
 
0.1%
3.1023401982
0.1%
3.0069377322
0.1%
2.890334722
0.1%
2.837333352
0.1%
2.6783292421
 
0.1%
2.5193251332
0.1%

pH
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct90
Distinct (%)5.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.987153406 × 10-15
Minimum-3.713692856
Maximum4.53570697
Zeros5
Zeros (%)0.3%
Negative817
Negative (%)51.1%
Memory size12.6 KiB
2023-04-09T20:21:19.002009image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum-3.713692856
5-th percentile-1.635103923
Q1-0.6607653611
median-0.01120631972
Q30.5733968175
95-th percentile1.677647188
Maximum4.53570697
Range8.249399826
Interquartile range (IQR)1.234162179

Descriptive statistics

Standard deviation1.000312842
Coefficient of variation (CV)5.033898435 × 1014
Kurtosis0.8342418229
Mean1.987153406 × 10-15
Median Absolute Deviation (MAD)0.6495590414
Skewness0.1895960525
Sum3.18101101 × 10-12
Variance1.000625782
MonotonicityNot monotonic
2023-04-09T20:21:19.299899image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-0.0761622238657
 
3.6%
0.31357320156
 
3.5%
-0.335985840453
 
3.3%
0.508440913448
 
3.0%
0.443485009348
 
3.0%
-0.14111812846
 
2.9%
0.0537495844245
 
2.8%
0.183661392743
 
2.7%
-0.206074032142
 
2.6%
-0.725721265339
 
2.4%
Other values (80)1122
70.2%
ValueCountFrequency (%)
-3.7136928561
 
0.1%
-2.9342220061
 
0.1%
-2.8692661021
 
0.1%
-2.8043101982
0.1%
-2.7393542944
0.3%
-2.6743983891
 
0.1%
-2.5444865814
0.3%
-2.4795306773
0.2%
-2.4145747734
0.3%
-2.3496188691
 
0.1%
ValueCountFrequency (%)
4.535706972
0.1%
3.8211920242
0.1%
3.4964125041
 
0.1%
3.0417211752
0.1%
2.8468534621
 
0.1%
2.7818975581
 
0.1%
2.651985753
0.2%
2.5870298464
0.3%
2.5220739421
 
0.1%
2.4571180384
0.3%

sulphates
Real number (ℝ)

HIGH CORRELATION

Distinct96
Distinct (%)6.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.754377476 × 10-16
Minimum-1.936507291
Maximum7.918676552
Zeros0
Zeros (%)0.0%
Negative964
Negative (%)60.3%
Memory size12.6 KiB
2023-04-09T20:21:19.586549image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum-1.936507291
5-th percentile-1.110324214
Q1-0.638219599
median-0.2251280607
Q30.4240157852
95-th percentile1.604277323
Maximum7.918676552
Range9.855183842
Interquartile range (IQR)1.062235384

Descriptive statistics

Standard deviation1.000312842
Coefficient of variation (CV)1.480984511 × 1015
Kurtosis11.72025073
Mean6.754377476 × 10-16
Median Absolute Deviation (MAD)0.4721046152
Skewness2.428672354
Sum1.080024958 × 10-12
Variance1.000625782
MonotonicityNot monotonic
2023-04-09T20:21:20.483968image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-0.343154214569
 
4.3%
-0.461180368368
 
4.3%
-0.697232675968
 
4.3%
-0.225128060761
 
3.8%
-0.579206522160
 
3.8%
-0.520193445255
 
3.4%
-0.756245752851
 
3.2%
-0.402167291451
 
3.2%
-0.63821959950
 
3.1%
-0.166114983848
 
3.0%
Other values (86)1018
63.7%
ValueCountFrequency (%)
-1.9365072911
 
0.1%
-1.7004549832
 
0.1%
-1.5824288296
 
0.4%
-1.5234157534
 
0.3%
-1.4053895995
 
0.3%
-1.3463765228
0.5%
-1.28736344516
1.0%
-1.22835036812
0.8%
-1.16933729118
1.1%
-1.11032421419
1.2%
ValueCountFrequency (%)
7.9186765521
 
0.1%
7.8006503981
 
0.1%
7.6236111672
0.1%
5.6761796291
 
0.1%
5.6171665521
 
0.1%
5.4991403991
 
0.1%
5.3221011681
 
0.1%
4.141839633
0.2%
4.0238134761
 
0.1%
3.9648003991
 
0.1%

alcohol
Real number (ℝ)

HIGH CORRELATION

Distinct65
Distinct (%)4.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.299426135 × 10-16
Minimum-1.898918597
Maximum4.202452586
Zeros0
Zeros (%)0.0%
Negative916
Negative (%)57.3%
Memory size12.6 KiB
2023-04-09T20:21:20.764073image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum-1.898918597
5-th percentile-1.147980605
Q1-0.8663788579
median-0.2093081152
Q30.6354971255
95-th percentile1.949638611
Maximum4.202452586
Range6.101371183
Interquartile range (IQR)1.501875983

Descriptive statistics

Standard deviation1.000312842
Coefficient of variation (CV)7.698112381 × 1015
Kurtosis0.2000293113
Mean1.299426135 × 10-16
Median Absolute Deviation (MAD)0.6570707427
Skewness0.8608288069
Sum1.580402476 × 10-13
Variance1.000625782
MonotonicityNot monotonic
2023-04-09T20:21:21.059505image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-0.8663788579139
 
8.7%
-0.9602461069103
 
6.4%
-0.58477711178
 
4.9%
-1.14798060572
 
4.5%
-0.397042613167
 
4.2%
0.0722936317167
 
4.2%
-1.05411335659
 
3.7%
0.541629876559
 
3.7%
-0.77251160959
 
3.7%
-0.6786443654
 
3.4%
Other values (55)842
52.7%
ValueCountFrequency (%)
-1.8989185972
 
0.1%
-1.8050513481
 
0.1%
-1.617316852
 
0.1%
-1.5234496012
 
0.1%
-1.33571510330
1.9%
-1.2887814781
 
0.1%
-1.24184785423
 
1.4%
-1.14798060572
4.5%
-1.1166915221
 
0.1%
-1.101046981
 
0.1%
ValueCountFrequency (%)
4.2024525861
 
0.1%
3.3576473457
0.4%
2.982178354
0.3%
2.9508892671
 
0.1%
2.8883111011
 
0.1%
2.7944438523
0.2%
2.7005766033
0.2%
2.6067093541
 
0.1%
2.5128421052
 
0.1%
2.4189748566
0.4%

quality
Real number (ℝ)

Distinct6
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.443669392 × 10-17
Minimum-3.265164633
Maximum2.928190347
Zeros0
Zeros (%)0.0%
Negative744
Negative (%)46.5%
Memory size12.6 KiB
2023-04-09T20:21:21.321276image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum-3.265164633
5-th percentile-0.7878226409
Q1-0.7878226409
median0.450848355
Q30.450848355
95-th percentile1.689519351
Maximum2.928190347
Range6.19335498
Interquartile range (IQR)1.238670996

Descriptive statistics

Standard deviation1.000312842
Coefficient of variation (CV)2.251096456 × 1016
Kurtosis0.2967081198
Mean4.443669392 × 10-17
Median Absolute Deviation (MAD)1.238670996
Skewness0.2178015755
Sum7.105427358 × 10-14
Variance1.000625782
MonotonicityNot monotonic
2023-04-09T20:21:21.515734image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
-0.7878226409681
42.6%
0.450848355638
39.9%
1.689519351199
 
12.4%
-2.02649363753
 
3.3%
2.92819034718
 
1.1%
-3.26516463310
 
0.6%
ValueCountFrequency (%)
-3.26516463310
 
0.6%
-2.02649363753
 
3.3%
-0.7878226409681
42.6%
0.450848355638
39.9%
1.689519351199
 
12.4%
2.92819034718
 
1.1%
ValueCountFrequency (%)
2.92819034718
 
1.1%
1.689519351199
 
12.4%
0.450848355638
39.9%
-0.7878226409681
42.6%
-2.02649363753
 
3.3%
-3.26516463310
 
0.6%

Interactions

2023-04-09T20:21:08.281744image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-09T20:20:30.893715image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-09T20:20:34.096455image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-09T20:20:37.906414image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-09T20:20:41.194839image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-09T20:20:44.130247image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-09T20:20:47.311718image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-09T20:20:52.180080image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-09T20:20:54.960149image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-09T20:20:57.942359image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-09T20:21:01.025244image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-09T20:21:04.864058image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-09T20:21:08.949097image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-09T20:20:31.135073image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-09T20:20:34.521637image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-09T20:20:38.149501image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-09T20:20:41.454610image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-09T20:20:44.375005image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-09T20:20:47.713947image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-09T20:20:52.409593image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-09T20:20:55.212538image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-09T20:20:58.179747image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-09T20:21:01.435851image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-09T20:21:05.101676image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-09T20:21:09.251464image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-09T20:20:31.361229image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-09T20:20:34.883892image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-09T20:20:38.372900image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-09T20:20:41.696844image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-09T20:20:44.625046image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-09T20:20:48.106207image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-09T20:20:52.633208image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-09T20:20:55.445989image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-09T20:20:58.411775image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-09T20:21:01.822295image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-09T20:21:05.836919image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-09T20:21:09.503013image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-09T20:20:31.618717image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-09T20:20:35.226541image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-09T20:20:38.614216image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-09T20:20:41.934379image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-09T20:20:44.865913image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-09T20:20:48.419365image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-09T20:20:52.868064image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-09T20:20:55.690320image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-09T20:20:58.639971image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-09T20:21:02.185476image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-09T20:21:06.067442image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-09T20:21:09.754859image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-09T20:20:31.865562image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-09T20:20:35.544435image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-09T20:20:38.855825image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-09T20:20:42.157562image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-09T20:20:45.110653image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-09T20:20:48.780868image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-09T20:20:53.083802image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-09T20:20:55.924582image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-09T20:20:58.878096image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-09T20:21:02.588745image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-09T20:21:06.300033image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-09T20:21:10.007551image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-09T20:20:32.120337image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-09T20:20:35.933141image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-09T20:20:39.103463image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-09T20:20:42.394867image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-09T20:20:45.356347image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-09T20:20:49.210789image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-09T20:20:53.343261image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-09T20:20:56.177800image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-09T20:20:59.106597image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-09T20:21:02.936354image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-09T20:21:06.548827image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-09T20:21:10.258496image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-09T20:20:32.359104image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-09T20:20:36.326972image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-09T20:20:39.342333image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-09T20:20:42.641625image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-09T20:20:45.614785image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-09T20:20:49.626069image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-09T20:20:53.580136image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-09T20:20:56.446220image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-09T20:20:59.340415image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-09T20:21:03.338979image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-09T20:21:06.810829image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-09T20:21:10.485562image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-09T20:20:32.605351image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-09T20:20:36.689322image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-09T20:20:39.568433image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-09T20:20:42.876043image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-09T20:20:45.838148image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-09T20:20:50.306015image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-09T20:20:53.788828image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-09T20:20:56.682545image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-09T20:20:59.558326image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-09T20:21:03.609129image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-09T20:21:07.042140image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-09T20:21:10.753456image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-09T20:20:32.866378image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-09T20:20:36.941112image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-09T20:20:39.825334image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-09T20:20:43.122792image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-09T20:20:46.096191image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-09T20:20:50.873530image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-09T20:20:54.038348image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-09T20:20:56.949967image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-09T20:20:59.803177image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-09T20:21:03.883293image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-09T20:21:07.296638image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-09T20:21:10.989985image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-09T20:20:33.100818image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-09T20:20:37.160204image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-09T20:20:40.461825image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-09T20:20:43.364942image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-09T20:20:46.328626image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-09T20:20:51.096366image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-09T20:20:54.256109image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-09T20:20:57.182089image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-09T20:21:00.017955image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-09T20:21:04.131910image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-09T20:21:07.531867image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-09T20:21:11.237083image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-09T20:20:33.374152image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-09T20:20:37.398317image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-09T20:20:40.692614image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-09T20:20:43.613386image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-09T20:20:46.569432image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-09T20:20:51.342671image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-09T20:20:54.484394image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-09T20:20:57.437126image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-09T20:21:00.347449image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-09T20:21:04.365971image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-09T20:21:07.776694image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-09T20:21:11.503229image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-09T20:20:33.754174image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-09T20:20:37.655282image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-09T20:20:40.943446image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-09T20:20:43.880620image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-09T20:20:46.920428image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-09T20:20:51.588390image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-09T20:20:54.724646image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-09T20:20:57.692568image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-09T20:21:00.692216image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-09T20:21:04.625383image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-09T20:21:08.018742image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Correlations

2023-04-09T20:21:21.735407image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2023-04-09T20:21:22.067237image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2023-04-09T20:21:22.415480image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2023-04-09T20:21:22.748405image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2023-04-09T20:21:11.909433image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
A simple visualization of nullity by column.
2023-04-09T20:21:12.479622image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

First rows

fixed acidityvolatile aciditycitric acidresidual sugarchloridesfree sulfur dioxidetotal sulfur dioxidedensitypHsulphatesalcoholquality
0-0.5283600.952113-1.391472-0.434776-0.243707-0.466193-0.3791330.5582741.287912-0.579207-0.960246-0.787823
1-0.2985471.921761-1.3914720.0559660.2238750.8726380.6243630.028261-0.7257210.128950-0.584777-0.787823
2-0.2985471.275329-1.186070-0.1543520.096353-0.0836690.2290470.134264-0.335986-0.048089-0.584777-0.787823
31.654856-1.3103991.484154-0.434776-0.2649600.1075920.4115000.664277-0.985545-0.461180-0.5847770.450848
4-0.5283600.952113-1.391472-0.434776-0.243707-0.466193-0.3791330.5582741.287912-0.579207-0.960246-0.787823
5-0.5283600.736635-1.391472-0.504882-0.264960-0.274931-0.1966790.5582741.287912-0.579207-0.960246-0.787823
6-0.2410940.413419-1.083370-0.645094-0.392483-0.0836690.381091-0.183745-0.076162-1.169337-0.960246-0.787823
7-0.5858130.682766-1.391472-0.925519-0.477498-0.083669-0.774449-1.1377690.508441-1.110324-0.3970431.689519
8-0.2985470.305681-1.288771-0.364670-0.307468-0.657454-0.8656760.0282610.313573-0.520193-0.8663791.689519
9-0.470907-0.1252740.4571442.509679-0.3499750.1075921.6886770.5582740.2486170.8371070.072294-0.787823

Last rows

fixed acidityvolatile aciditycitric acidresidual sugarchloridesfree sulfur dioxidetotal sulfur dioxidedensitypHsulphatesalcoholquality
1589-0.9879841.086786-0.3644633.701483-0.3074681.2551610.9892710.505273-0.141118-0.697233-1.147981-0.787823
1590-1.1603430.144073-0.621215-0.504882-0.2224530.968269-0.348724-1.9115890.0537500.9551331.1048330.450848
1591-1.6774211.167590-0.929318-0.5749880.0325920.011961-0.622404-1.4451772.327206-0.5792071.1048330.450848
1592-1.160343-0.071405-0.723916-0.154352-0.2437071.255161-0.196679-0.5335540.7033090.5420420.5416300.450848
1593-0.8730780.521158-0.980669-0.434776-0.4137361.159531-0.257497-0.1254430.7033090.955133-0.8663790.450848
1594-1.2177960.413419-0.980669-0.3646700.0538451.542054-0.075043-0.9787650.898176-0.4611800.072294-0.787823
1595-1.3901550.144073-0.877968-0.224458-0.5412592.2114690.137820-0.8621621.3528680.6010550.7293640.450848
1596-1.160343-0.071405-0.723916-0.154352-0.2437071.255161-0.196679-0.5335540.7033090.5420420.5416300.450848
1597-1.3901550.655831-0.775267-0.364670-0.2649601.542054-0.075043-0.6766571.6776470.305990-0.209308-0.787823
1598-1.332702-1.1487911.0219990.757027-0.4349900.203223-0.135861-0.6660570.5084410.0109240.5416300.450848

Duplicate rows

Most frequently occurring

fixed acidityvolatile aciditycitric acidresidual sugarchloridesfree sulfur dioxidetotal sulfur dioxidedensitypHsulphatesalcoholquality# duplicates
21-0.930531-0.340751-0.159061-0.574988-0.2224530.203223-0.379133-1.0317670.508441-0.3431540.1661610.4508484
51-0.643266-0.8794450.970649-0.294564-0.2862140.777007-0.075043-0.7455590.5733971.1321730.5416301.6895194
61-0.6432660.925178-0.723916-0.364670-0.243707-0.370562-0.804858-0.681958-0.141118-0.697233-0.303175-0.7878234
78-0.470907-0.071405-1.288771-0.574988-0.073677-0.274931-0.470360-0.7243590.313573-0.6972330.0722940.4508484
5-1.332702-0.125274-1.391472-0.785306-0.647527-0.083669-0.622404-1.2013710.313573-1.228350-0.866379-0.7878233
12-1.1028900.628897-0.313113-0.504882-0.137438-0.179300-0.4703600.0759621.8075590.010924-0.584777-0.7878233
37-0.7581720.682766-1.288771-0.294564-0.456244-0.753085-0.6528130.2402661.0280880.069937-0.8663790.4508483
38-0.7581720.898243-1.032019-0.0141400.075099-0.083669-0.774449-0.5441540.443485-0.3431540.8232320.4508483
58-0.6432660.575027-1.391472-0.4347760.202621-0.179300-0.2574970.0017600.378529-0.461180-1.3357150.4508483
101-0.2985470.413419-0.056360-0.364670-0.1586921.4464232.570537-0.279147-0.660765-0.815259-0.490910-0.7878233